In [3]:
import requests
import pandas as pd
from datetime import datetime, timedelta
import plotly.graph_objects as go
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings("ignore")
# Settings
symbol = 'BTCIRT'
resolution = '360' # 8-hour timeframe
to_date = datetime.now()
from_date = to_date - timedelta(days=365) # 30 days of data
# Convert timestamps
to_timestamp = int(to_date.timestamp())
from_timestamp = int(from_date.timestamp())
# Request data from Nobitex
url = 'https://api.nobitex.ir/market/udf/history'
params = {
'symbol': symbol,
'resolution': resolution,
'from': from_timestamp,
'to': to_timestamp
}
response = requests.get(url, params=params)
data = response.json()
# Validate response
if data['s'] == 'ok':
# Create DataFrame
df = pd.DataFrame({
'timestamp': data['t'],
'open': data['o'],
'high': data['h'],
'low': data['l'],
'close': data['c'],
'volume': data['v']
})
# Convert timestamp to datetime
df['timestamp'] = pd.to_datetime(df['timestamp'], unit='s')
# Compute EMA
fast_ema_period = 10
slow_ema_period = 30
df['fast_ema'] = df['close'].ewm(span=fast_ema_period, adjust=False).mean()
df['slow_ema'] = df['close'].ewm(span=slow_ema_period, adjust=False).mean()
# Implement strategy logic
df['position'] = 0 # Initial position
# Trend-following conditions
df.loc[(df['close'] > df['fast_ema']) & (df['fast_ema'] > df['slow_ema']), 'position'] = 1 # Open long trend-following
df.loc[(df['close'] < df['fast_ema']) & (df['fast_ema'] > df['slow_ema']), 'position'] = -1 # Close long, open short
# Mean-reversion conditions
df.loc[(df['close'] > df['fast_ema']) & (df['fast_ema'] < df['slow_ema']), 'position'] = 1 # Open long mean-reversion
df.loc[(df['close'] < df['fast_ema']) & (df['fast_ema'] < df['slow_ema']), 'position'] = -1 # Close long mean-reversion
# Calculate returns
df['returns'] = df['close'].pct_change()
df['strategy_returns'] = df['position'].shift(1) * df['returns']
# Calculate cumulative returns
df['cumulative_strategy_returns'] = (1 + df['strategy_returns']).cumprod()
# Plot price with EMA and signals
fig = go.Figure()
# Candlestick chart
fig.add_trace(go.Candlestick(
x=df['timestamp'],
open=df['open'],
high=df['high'],
low=df['low'],
close=df['close'],
name='Price'
))
# Fast EMA
fig.add_trace(go.Scatter(
x=df['timestamp'],
y=df['fast_ema'],
mode='lines',
name='Fast EMA',
line=dict(color='blue', width=2)
))
# Slow EMA
fig.add_trace(go.Scatter(
x=df['timestamp'],
y=df['slow_ema'],
mode='lines',
name='Slow EMA',
line=dict(color='red', width=2)
))
# Chart settings
fig.update_layout(
title='Bitcoin to Rial with Dual EMA Strategy',
xaxis_title='Date',
yaxis_title='Price (IRR)',
xaxis_rangeslider_visible=False,
template='plotly_dark'
)
fig.show()
# Plot cumulative strategy returns
plt.figure(figsize=(10, 5))
plt.plot(df['timestamp'], df['cumulative_strategy_returns'], label='Strategy Returns', color='green')
plt.xlabel('Date')
plt.ylabel('Cumulative Returns')
plt.title('Cumulative Strategy Returns')
plt.legend()
plt.grid()
plt.show()
# Calculate trade P&Ls
df['trade_pnl'] = df['strategy_returns'].dropna()
# Plot distribution of trade P&Ls
plt.figure(figsize=(10, 5))
plt.hist(df['trade_pnl'], bins=30, color='blue', alpha=0.7, edgecolor='black')
plt.xlabel('Trade P&L')
plt.ylabel('Frequency')
plt.title('Distribution of Trade P&Ls')
plt.grid()
plt.show()
else:
print('Error in fetching data:', data.get('errmsg', 'Unknown error'))
